From eeb097662a62285c884c024851ec83c38bcb5c4d Mon Sep 17 00:00:00 2001 From: Ryan Date: Sun, 29 Dec 2024 12:40:01 +0800 Subject: [PATCH] finish cuda programming, took around 1.8 hrs --- 2-2-cuda.md | 138 +++++++++++++++++++++++++++++++++++++++++++ assets/code/a.out | Bin 0 -> 1002712 bytes assets/code/test.cu | 10 ++++ assets/code/test1.cu | 39 ++++++++++++ 4 files changed, 187 insertions(+) create mode 100644 2-2-cuda.md create mode 100755 assets/code/a.out create mode 100644 assets/code/test.cu create mode 100644 assets/code/test1.cu diff --git a/2-2-cuda.md b/2-2-cuda.md new file mode 100644 index 0000000..e1e374e --- /dev/null +++ b/2-2-cuda.md @@ -0,0 +1,138 @@ +# CUDA + +## Definition + +- Compute Unified Device Architecture +- Is a general purpose **parallel** programming platform and model + +## Scalability with CUDA + +- GPU is has an array of Streaming Multiprocessors +- A program can be partitioned into blocks that execute independently +- Allow performance to scale with the number of GPU multiprocessors + +## Architecture + +### Kernel + +- CUDA extends C by allow to define C functions, which is called a kernel + - Kernel is executed in parallel, by N different CUDA threads, rather than + only once in serial + - Defined with specifier `__global__` +- Number of threads is given using `<<>>`, and used with + `func_name<<>>(params);` + - They take an integer or dim3, so it cal also be called Dimension of + Blocks, or Dimension of Threads +- Thread number is given an ID, accessible by `threadIdx` variable +- Example: + ```c + // Add array A and B of size N together, store it in C + #define N 2 + // Kernel definition + __global__ void VecAdd(float* A, float* B, float* C) { + // Each thread performs one pair wise addition + int i = threadIdx.x; + // The array is a float, meaning we need to allocate memory on GPU + C[i] = A[i] + B[i]; + } + int main() { + ... + // Kernel invocation with N threads + VecAdd<<<1, N>>>(A, B, C); + ... + } + ``` + +### Device code and Host code + +- Compiler `nvcc` separates source code into host and device code + - Device code (kernels): definition marked with `__global__`, called from + host code (with `func<<>>(a, b)`), and run on device (GPU) + - Host code: normal C code, processed by system compiler like `gcc` or + `clang` +- If the device code definitions are used, the compiled code execute on both CPU + and GPU + +### Thread hierarchy + +- `threadIdx` is a 3 component vector +- Enables threads to be identified in 1-3 dimension blocks, which can form a + **grid** of thread blocks +- Number of threads depend on the size of data being processed +- Features: + - Maximum number of threads per block: because they reside on the same + block, on the same processor core + - A kernel can be executed by multiple equally shaped thread blocks, so the + total number of threads is threads per block times blocks +- Example: + ```c + #define N 10 + // Kernel definition + __global__ void MatAdd(float A[N][N], float B[N][N], float C[N][N]) { + int i = threadIdx.x; + int j = threadIdx.y; + C[i][j] = A[i][j] + B[i][j]; + } + int main() { + ... + // Kernel invocation with one block of N * N * 1 threads + int numBlocks = 1; + dim3 threadsPerBlock(N, N); + MatAdd<<>>(A, B, C); + ... + } + ``` +- A complete example: + + ```c + + ``` + +### Block Hierarchy + +- Blocks can be represented in 1-3 dimensional **grids**, size is dependent on + the input data +- Accessing + - Block index: `blockIdx` + - Dimension: `blockDim` +- Example: multi block kernel: + ```c + #define N 16 + // Kernel definition + __global__ void MatAdd(float A[N][N], float B[N][N], + float C[N][N]) { + int i = blockIdx.x * blockDim.x + threadIdx.x; + int j = blockIdx.y * blockDim.y + threadIdx.y; + if (i < N && j < N) + C[i][j] = A[i][j] + B[i][j]; + } + int main() { + ... + // Kernel invocation + dim3 threadsPerBlock(16, 16); + dim3 numBlocks(N / threadsPerBlock.x, N / threadsPerBlock.y); + MatAdd<<>>(A, B, C); + ... + } + ``` + +### Memory Hierarchy + +- Thread: Registers, Local memory +- Block of threads: Shared memory +- All Blocks: Global memory + +### GPU vs. CPU + +- GPU has more stream processors, and each processor has much more ALUs +- TODO: see code in assets/code, and figure shit out, and index array + +## Coordinating with CPU + +- GPU kernel launch are **asynchronous**, while control returns to CPU + immediately +- CPU need to **synchronize** before using the results + - `cudaMemcpy()`: block and copy + - `cudaMemcpyAsync()`: copy but don't block + - `cudaDeviceSynchronize()`: Block the CPU until all CUDA calls before have + completed diff --git a/assets/code/a.out b/assets/code/a.out new file mode 100755 index 0000000000000000000000000000000000000000..b6d11fd750800a9148297fc4e22fe07201cea300 GIT binary patch literal 1002712 zcmeF437k{a_4wbgNWdjgX^j?Zsyo+K5)8_V+WjR$qi_suttM{Li`fBy)2h$gjWu^85Th z9c7a9z2}{K?zzjm_r7JW^arPpXlT$XKO?p8X(;9Hk;Fp@tv)+_(=?CPr0oH}57fS; 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